[Congressional Bills 116th Congress]
[From the U.S. Government Publishing Office]
[S. 2904 Reported in Senate (RS)]
<DOC>
Calendar No. 580
116th CONGRESS
2d Session
S. 2904
[Report No. 116-289]
To direct the Director of the National Science Foundation to support
research on the outputs that may be generated by generative adversarial
networks, otherwise known as deepfakes, and other comparable techniques
that may be developed in the future, and for other purposes.
_______________________________________________________________________
IN THE SENATE OF THE UNITED STATES
November 20, 2019
Ms. Cortez Masto (for herself and Mr. Moran) introduced the following
bill; which was read twice and referred to the Committee on Commerce,
Science, and Transportation
November 9, 2020
Reported by Mr. Wicker, with an amendment
[Strike out all after the enacting clause and insert the part printed
in italic]
_______________________________________________________________________
A BILL
To direct the Director of the National Science Foundation to support
research on the outputs that may be generated by generative adversarial
networks, otherwise known as deepfakes, and other comparable techniques
that may be developed in the future, and for other purposes.
Be it enacted by the Senate and House of Representatives of the
United States of America in Congress assembled,
<DELETED>SECTION 1. SHORT TITLE.</DELETED>
<DELETED> This Act may be cited as the ``Identifying Outputs of
Generative Adversarial Networks Act'' or the ``IOGAN Act''.</DELETED>
<DELETED>SEC. 2. FINDINGS.</DELETED>
<DELETED> Congress finds the following:</DELETED>
<DELETED> (1) Research gaps currently exist on the
underlying technology needed to develop tools to identify
authentic videos, voice reproduction, or photos from
manipulated or synthesized content, including those generated
by generative adversarial networks.</DELETED>
<DELETED> (2) The National Science Foundation's focus to
support research in artificial intelligence through computer
and information science and engineering, cognitive science and
psychology, economics and game theory, control theory,
linguistics, mathematics, and philosophy, is building a better
understanding of how new technologies are shaping the society
and economy of the United States.</DELETED>
<DELETED> (3) The National Science Foundation has identified
the ``10 Big Ideas for NSF Future Investment'' including
``Harnessing the Data Revolution'' and the ``Future of Work at
the Human-Technology Frontier'', in with artificial
intelligence is a critical component.</DELETED>
<DELETED> (4) The outputs generated by generative
adversarial networks should be included under the umbrella of
research described in paragraph (3) given the grave national
security and societal impact potential of such
networks.</DELETED>
<DELETED> (5) Generative adversarial networks are not likely
to be utilized as the sole technique of artificial intelligence
or machine learning capable of creating credible deepfakes.
Other comparable techniques may be developed in the future to
produce similar outputs.</DELETED>
<DELETED>SEC. 3. NSF SUPPORT OF RESEARCH ON MANIPULATED OR SYNTHESIZED
CONTENT AND INFORMATION SECURITY.</DELETED>
<DELETED> The Director of the National Science Foundation, in
consultation with other relevant Federal agencies, shall support merit-
reviewed and competitively awarded research on manipulated or
synthesized content and information authenticity, which may include--
</DELETED>
<DELETED> (1) fundamental research on digital forensic tools
or other technologies for verifying the authenticity of
information and detection of manipulated or synthesized
content, including content generated by generative adversarial
networks;</DELETED>
<DELETED> (2) fundamental research on technical tools for
identifying manipulated or synthesized content, such as
watermarking systems for generated media;</DELETED>
<DELETED> (3) social and behavioral research related to
manipulated or synthesized content, including the ethics of the
technology and human engagement with the content;</DELETED>
<DELETED> (4) research on public understanding and awareness
of manipulated and synthesized content, including research on
best practices for educating the public to discern authenticity
of digital content; and</DELETED>
<DELETED> (5) research awards coordinated with other Federal
agencies and programs, including the Networking and Information
Technology Research and Development Program, the Defense
Advanced Research Projects Agency, and the Intelligence
Advanced Research Projects Agency.</DELETED>
<DELETED>SEC. 4. NIST SUPPORT FOR RESEARCH AND STANDARDS ON GENERATIVE
ADVERSARIAL NETWORKS.</DELETED>
<DELETED> (a) In General.--The Director of the National Institute of
Standards and Technology shall support research for the development of
measurements and standards necessary to accelerate the development of
the technological tools to examine the function and outputs of
generative adversarial networks or other technologies that synthesize
or manipulate content.</DELETED>
<DELETED> (b) Outreach.--The Director of the National Institute of
Standards and Technology shall conduct outreach--</DELETED>
<DELETED> (1) to receive input from private, public, and
academic stakeholders on fundamental measurements and standards
research necessary to examine the function and outputs of
generative adversarial networks; and</DELETED>
<DELETED> (2) to consider the feasibility of an ongoing
public and private sector engagement to develop voluntary
standards for the function and outputs of generative
adversarial networks or other technologies that synthesize or
manipulate content.</DELETED>
<DELETED>SEC. 5. REPORT ON FEASIBILITY OF PUBLIC-PRIVATE PARTNERSHIP TO
DETECT MANIPULATED OR SYNTHESIZED CONTENT.</DELETED>
<DELETED> Not later than 1 year after the date of enactment of this
Act, the Director of the National Science Foundation and the Director
of the National Institute of Standards and Technology shall jointly
submit to the Committee on Science, Space, and Technology of the House
of Representatives, the Subcommittee on Commerce, Justice, Science, and
Related Agencies of the Committee on Appropriations of the House of
Representatives, the Committee on Commerce, Science, and Transportation
of the Senate, and the Subcommittee on Commerce, Justice, Science, and
Related Agencies of the Committee on Appropriations of the Senate a
report containing--</DELETED>
<DELETED> (1) the Directors' findings with respect to the
feasibility for research opportunities with the private sector,
including digital media companies to detect the function and
outputs of generative adversarial networks or other
technologies that synthesize or manipulate content;
and</DELETED>
<DELETED> (2) any policy recommendations of the Directors
that could facilitate and improve communication and
coordination between the private sector, the National Science
Foundation, and relevant Federal agencies through the
implementation of innovative approaches to detect digital
content produced by generative adversarial networks or other
technologies that synthesize or manipulate content.</DELETED>
<DELETED>SEC. 6. GENERATIVE ADVERSARIAL NETWORK DEFINED.</DELETED>
<DELETED> In this Act, the term ``generative adversarial network''
means, with respect to artificial intelligence, the machine learning
process of attempting to cause a generator artificial neural network
(referred to in this paragraph as the ``generator'') and a
discriminator artificial neural network (referred to in this paragraph
as a ``discriminator'') to compete against each other to become more
accurate in their function and outputs, through which the generator and
discriminator create a feedback loop, causing the generator to produce
increasingly higher-quality artificial outputs and the discriminator to
increasingly improve in detecting such artificial outputs.</DELETED>
SECTION 1. SHORT TITLE.
This Act may be cited as the ``Identifying Outputs of Generative
Adversarial Networks Act'' or the ``IOGAN Act''.
SEC. 2. FINDINGS.
Congress finds the following:
(1) Gaps currently exist on the underlying research needed
to develop tools that detect videos, audio files, or photos
that have manipulated or synthesized content, including those
generated by generative adversarial networks. Research on
digital forensics is also needed to identify, preserve,
recover, and analyze the provenance of digital artifacts.
(2) The National Science Foundation's focus to support
research in artificial intelligence through computer and
information science and engineering, cognitive science and
psychology, economics and game theory, control theory,
linguistics, mathematics, and philosophy, is building a better
understanding of how new technologies are shaping the society
and economy of the United States.
(3) The National Science Foundation has identified the ``10
Big Ideas for NSF Future Investment'' including ``Harnessing
the Data Revolution'' and the ``Future of Work at the Human-
Technology Frontier'', with artificial intelligence is a
critical component.
(4) The outputs generated by generative adversarial
networks should be included under the umbrella of research
described in paragraph (3) given the grave national security
and societal impact potential of such networks.
(5) Generative adversarial networks are not likely to be
utilized as the sole technique of artificial intelligence or
machine learning capable of creating credible deepfakes. Other
techniques may be developed in the future to produce similar
outputs.
SEC. 3. NSF SUPPORT OF RESEARCH ON MANIPULATED OR SYNTHESIZED CONTENT
AND INFORMATION SECURITY.
The Director of the National Science Foundation, in consultation
with other relevant Federal agencies, shall support merit-reviewed and
competitively awarded research on manipulated or synthesized content
and information authenticity, which may include--
(1) fundamental research on digital forensic tools or other
technologies for verifying the authenticity of information and
detection of manipulated or synthesized content, including
content generated by generative adversarial networks;
(2) fundamental research on technical tools for identifying
manipulated or synthesized content, such as watermarking
systems for generated media;
(3) social and behavioral research related to manipulated
or synthesized content, including human engagement with the
content;
(4) research on public understanding and awareness of
manipulated and synthesized content, including research on best
practices for educating the public to discern authenticity of
digital content; and
(5) research awards coordinated with other federal agencies
and programs, including the Defense Advanced Research Projects
Agency and the Intelligence Advanced Research Projects Agency,
with coordination enabled by the Networking and Information
Technology Research and Development Program.
SEC. 4. NIST SUPPORT FOR RESEARCH AND STANDARDS ON GENERATIVE
ADVERSARIAL NETWORKS.
(a) In General.--The Director of the National Institute of
Standards and Technology shall support research for the development of
measurements and standards necessary to accelerate the development of
the technological tools to examine the function and outputs of
generative adversarial networks or other technologies that synthesize
or manipulate content.
(b) Outreach.--The Director of the National Institute of Standards
and Technology shall conduct outreach--
(1) to receive input from private, public, and academic
stakeholders on fundamental measurements and standards research
necessary to examine the function and outputs of generative
adversarial networks; and
(2) to consider the feasibility of an ongoing public and
private sector engagement to develop voluntary standards for
the function and outputs of generative adversarial networks or
other technologies that synthesize or manipulate content.
SEC. 5. REPORT ON FEASIBILITY OF PUBLIC-PRIVATE PARTNERSHIP TO DETECT
MANIPULATED OR SYNTHESIZED CONTENT.
Not later than 1 year after the date of enactment of this Act, the
Director of the National Science Foundation and the Director of the
National Institute of Standards and Technology shall jointly submit to
the Committee on Science, Space, and Technology of the House of
Representatives, the Subcommittee on Commerce, Justice, Science, and
Related Agencies of the Committee on Appropriations of the House of
Representatives, the Committee on Commerce, Science, and Transportation
of the Senate, and the Subcommittee on Commerce, Justice, Science, and
Related Agencies of the Committee on Appropriations of the Senate a
report containing--
(1) the Directors' findings with respect to the feasibility
for research opportunities with the private sector, including
digital media companies to detect the function and outputs of
generative adversarial networks or other technologies that
synthesize or manipulate content; and
(2) any policy recommendations of the Directors that could
facilitate and improve communication and coordination between
the private sector, the National Science Foundation, and
relevant Federal agencies through the implementation of
innovative approaches to detect digital content produced by
generative adversarial networks or other technologies that
synthesize or manipulate content.
SEC. 6. GENERATIVE ADVERSARIAL NETWORK DEFINED.
In this Act, the term ``generative adversarial network'' means,
with respect to artificial intelligence, the machine learning process
of attempting to cause a generator artificial neural network (referred
to in this paragraph as the ``generator'' and a discriminator
artificial neural network (referred to in this paragraph as a
``discriminator'') to compete against each other to become more
accurate in their function and outputs, through which the generator and
discriminator create a feedback loop, causing the generator to produce
increasingly higher-quality artificial outputs and the discriminator to
increasingly improve in detecting such artificial outputs.
Calendar No. 580
116th CONGRESS
2d Session
S. 2904
[Report No. 116-289]
_______________________________________________________________________
A BILL
To direct the Director of the National Science Foundation to support
research on the outputs that may be generated by generative adversarial
networks, otherwise known as deepfakes, and other comparable techniques
that may be developed in the future, and for other purposes.
_______________________________________________________________________
November 9, 2020
Reported with an amendment